{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Exercícios\n",
    "\n",
    "1 - Aplique os algoritmos K-means [1] e AgglomerativeClustering [2] em qualquer dataset que você desejar (recomendação: iris). Compare os resultados utilizando métricas de avaliação de clusteres (completeness e homogeneity, por exemplo) [3].\n",
    "\n",
    "* [1] http://scikit-learn.org/stable/modules/clustering.html#k-means\n",
    "\n",
    "* [2] http://scikit-learn.org/0.17/modules/clustering.html#hierarchical-clustering\n",
    "\n",
    "* [3] http://scikit-learn.org/stable/modules/clustering.html#clustering-evaluation\n",
    "\n",
    "2 - Qual o valor de K (número de clusteres) você escolheu para a questão anterior? Desenvolva o Método do Cotovelo (não utilizar lib!) e descubra o K mais adequado. Após descobrir, aplique novamente o K-means com o K adequado. \n",
    "\n",
    "* Ajuda: atributos do [k-means](http://scikit-learn.org/0.17/modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans)\n",
    "\n",
    "3 - Após a questão 2, você aplicou o algoritmo com K apropriado. Refaça o cálculo das métricas de acordo com os resultados de clusters obtidos com a questão anterior e verifique se o resultado melhorou.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bibliotecas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn import metrics\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn.decomposition import PCA\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1ª Questão:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Carregando o Dataset (Glass)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Carregando o Wine Dataset (https://archive.ics.uci.edu/ml/datasets/Wine)\n",
    "data = pd.read_csv(\"wine.data\")\n",
    "X = data.iloc[:,1:].values\n",
    "y = data.iloc[:,0].values\n",
    "\n",
    "# Pre-processing the data (for PCA)\n",
    "X = (X - X.mean(axis=0)) / X.std(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualização dos Dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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PVoteIbZDhKwoSsW/n0Lt49mGTMt9PpxOJ4qiEA6HcbvdZa0pxGKxgn4t25UdKcj5hDj7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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f35612d7400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotando uma visualização 3-Dimensional dos Dados\n",
    "# Podemos observar que os dados (em 3-Dimensões) são extremamente superpostos\n",
    "pcaData = PCA(n_components=3).fit_transform(X)\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.scatter(pcaData[:,0], pcaData[:,1], pcaData[:,2], c=y, cmap=plt.cm.Dark2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Aplicação do K-Means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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oiszPzzM+Pk5NTU1WId7K9gt5vyKyiv/F0tIS3d3dWW9cik9Gvtst5Uy9n80N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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f359e6720b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Criamos o objeto da classe KMeans\n",
    "kmeans = KMeans(n_clusters=2, random_state=0)\n",
    "\n",
    "# Realizamos a Clusterização\n",
    "kmeans.fit(X)\n",
    "clts = kmeans.predict(X)\n",
    "\n",
    "# Plotando uma visualização 3-Dimensional dos Dados, agora com os clusteres designados pelo K-Means\n",
    "# Compare a visualização com o gráfico da celula de cima\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.scatter(pcaData[:,0], pcaData[:,1], pcaData[:,2], c=clts, cmap=plt.cm.Dark2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Métricas de Avaliação"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "### Avaliação (2 Clusters) ###\n",
      "Homogeneity: \t0.402\n",
      "Completeness: \t0.664\n",
      "V-Measure: \t0.501\n"
     ]
    }
   ],
   "source": [
    "# Utilizamos três métricas de avaliação dos Clusteres, com base nos dados já classificados: \n",
    "# -> Homogeneity: porcentagem relativa ao objetivo de ter, em cada cluster, apenas membros de uma mesma classe\n",
    "# -> Completeness: porcentagem relativa ao objetivo de ter todos os membros de uma classe no mesmo cluster\n",
    "# -> V-Measure: medida que relaciona Homogeneity com Completeness, e é equivalente à uma métrica conhecida como NMI (Normalized Mutual Information).\n",
    "homoScore = metrics.homogeneity_score(y, clts)\n",
    "complScore = metrics.completeness_score(y, clts)  \n",
    "vMeasureScore = metrics.v_measure_score(y, clts)\n",
    "\n",
    "print(\"### Avaliação ({0} Clusters) ###\".format(kmeans.n_clusters))\n",
    "print(\"Homogeneity: \\t{0:.3}\".format(homoScore))\n",
    "print(\"Completeness: \\t{0:.3}\".format(complScore))\n",
    "print(\"V-Measure: \\t{0:.3}\".format(vMeasureScore))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 2ª Questão"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implementando o Método do Cotovelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "........."
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f355dc6a7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Método do Cotevelo baseado na Inertia (Soma Quadrática da Distância Intra-Cluster de cada Ponto)\n",
    "numK = np.arange(1,10); inertias = []\n",
    "for i in numK:\n",
    "    print(\".\", end=\"\")\n",
    "    kmeans.n_clusters = i\n",
    "    kmeans.fit(X)\n",
    "    \n",
    "    inertias.append(kmeans.inertia_)\n",
    "    \n",
    "# Plotagens\n",
    "plt.figure()\n",
    "plt.title(\"Elbow Method\")\n",
    "plt.xlabel(\"Num of Clusters\"); plt.ylabel(\"Inertia\")\n",
    "plt.plot(numK, inertias, 'bo-')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Questão 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "### Avaliação (3 Clusters) ###\n",
      "Homogeneity: \t0.878\n",
      "Completeness: \t0.872\n",
      "V-Measure: \t0.875\n"
     ]
    },
    {
     "data": {
      "image/png": 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RdDlYLBZ+53d+h+PHjxONRjlx4gSPPvpoSR27Jb3fumx1g0kmkwwMDBCJRIwJ\nzqsdWOVEyOWUscmyzPT0NMPDw3i93oJCvJbtl/J8XWR1/4v5+Xm6u7sLXrh0n4xit1vJmXovTd3m\nq3fO47bYyKoK1wKT/KeTP06zs7TbUFnN8ifnn2EmNoCmKVyZ/Humonf45L78CEfTNNLqQofgFf8E\naUWh3b2QqgmR5IWJmxz0tt41HlrG5EeW5bxJ1vrsvdzc50q+HlvB+nIr3OlUwseiqamJs2fPcvbs\n2jy0W1tbaW1dWEzWp2BPTk6agrwSwWCQxsbGvAhvNSwWS8lXzlLyzpqmEQwGmZubw2KxcPz48VWn\nQax3hAwwOjpKMBhkx44dnDlzZlkB2Az7Tf23+4fxm3htTpyWhTuRqUSYH04P8EBzL41OD7YivSWG\nA5eYi48gCRYE0Yqmqbw18nU+tvufYZEWtn3FN8GXrr5IMJ3ALcNPeU8Sl9PMJCLEsmnG4kFsokSN\nzcmne0/gWWFqicVioba2Ns9DITf3uZKvhx7hbWaEuhUidKiMF/J6+FiMjIxw5coVTp8+XfFt62wL\nQW5vby+rrbnclMVK6C3FukWnflUthpVu/TVN49atW0xNTVFbW8uhQ4eKNiPSrTn9fn+eR/JKrNV+\nsxz07UiCgC7vGuBPx/n26DXe9o1TZ3PxS333U2dfvT44q6QQELirMQIIArKWxYINfyrOF9/+HySV\nLC6LjdlUjK/cOQcaqALEsimqrHaON/cyEvXzrZGr/Ozu+0r+TIVyn7mWmXpLcjwe55133lmyiLhR\nUfNWiNBh7dNCwuFw3pzDShCLxXjqqaf4vd/7vXVdMNwWgryRU0OWE0BN05ibm2NoaIiamhqOHTuG\nJElcuXKl5H0rxI9+9CMuXbpkpFqGhob4xCc+sWJkmtvt19raSktLC83NzRUfXFqpHLLOE12H+KNb\nb5BWsoQzKZJylr6mFtxWO3OpKH839A73NXXxo5khBEHgx9r2sO/9SgRV03h9ZpD3gtO4JAlF8KAp\nAURBREOjx3sMh8VNPJvmG0OX8aXjeG0urKKES7LgyyT5SEsvU8kIqqpil6zUO9wIwGDEX7HPWMjX\n48KFC/T19RmVBGNjY8aoMb2SILckr9LR7FYa37SW/YhGo+zcubNi+5PNZnnqqaf4zGc+w0/91E9V\nbLuF2BaCXA6Vmjyd6+1QXV2d11KsqmpFOumy2SxXrlyhtrbWEL/p6Wl8Pl/B7WuaxtTUFCMjI3nd\nfrdv3y4jwhurAAAgAElEQVR6f0odcrpYvNcyU+9DLb24LTbOz40ym4wwn4oZA05rrU7eDU5zKzxL\njdWJhsaf3H6Lf973IDurG/iH8Rv8w8QNaqxOUkoWqeqzdPMaifQU3d5jPL7/10gpWf77zR9xOzRL\nRpHxp2J4HW6yivJ+y7SVBrubWCaNRZTIqgppRaZhjV17g/6LXJn8B+wWJ2e7/gkN7h1LPr/u67F4\nkrVeSRAMBpmYmCCdThsDUivl67FVvJBL8RAvRCVTFpqm8bnPfY6+vj5+9Vd/tSLbXIltIcgbFSHn\njn7ShXhoaAiPx5MnxDqVihx1scutgND/O1cIcx3Y6urqlnT7rVdeuFDKYq23v8cbOjne0Mnt0Cx/\ndOsNFFVFEkUCmQRZVabOXk21bSEnn1EVLvsn6Kmq55Xpftqc1VhEiRocTCXC3L/n/+Bo/d2yp2uB\nSWaSEfpqmplNRpmIh/AlY9gQ8Nqc/GhmEKsoEZczVNschNJJnBYbT/UcLfvzvDfzKn9z7d+iqDKg\ncXnye3z25B/SVdub15lXiOU63fQBqbFYLK+Bolxfj60yLaQSi3qVHHD6ta99jUOHDnH06MLv/5u/\n+Zt88pOfrMj2F7MtBLkcyhXkRCJhRMQej4fDhw+vKd9VDHa7nV27dtHf34/T6SSdTlNVVUVLSwvT\n09PGjL+BgQHDga3QAmKpaYhyBDmVStHf308wGEQUxaIrDAACmQQjyTBNqRiNjgXx2VPTxMfa9/Hy\n1B0AWl019FY1MBYLGq9TNNVY6JMEATXn4qAB4qK3U7X3/S/ULGeauhmNBphLxbhfrOe8FiCaTZHV\nVBocblySjad7T7CrusGI0lcink0Tzqaot7vz/Jt/MPj/omkqNsnBXLaGq4ke3nzzb+mt7eULRx4t\ny4x/JV8Pfe7ecr4eHo9niehtlRxyJRb1KuWF/OCDD25o5cm2EORyIuRSo1dN04jFYkxMTJBKpdZN\niJfrBnz00Ueprq5mYmKC2tpaHnjgARwOB9lslgsXLuByuThy5MiK+7ReeWF9cfH27dsEAgF27tzJ\n7t27EQQhr8JgdnbWEIdcYfB4PFwOTPLnt98iFovzvStT/E87T3C2uQdBEHiscz8PtOwkrch47S6m\n4mH++83XmU6E0QCnxXr3uR19fHPkGk7JSkaVaXJ62F3dlLe/XruDgYiPhJxGRMRrd/FP992PMjHP\ntXSUds9CdKVpGr5UDK/NWZQYvzE7xB/fehNN03BZbPz64R+jt7phQSSzSVQNkqqNq+ldCMhUWSCS\nSfFfr77I79//00V916uR6+vR1HT3cy/29RgaGlri67FVBHkrRcgbzbYQ5PVE0zT8fj+Dg4NYLBbq\n6uo4dGj9vAX0hcNCnYUPPvig8f+hUIiLFy+SyWQ4fvz4ktvZQqxHykKWZSYnJ5mbm6Ovr8/w4chk\nMstWGOjioE9O9kXD/PHcNaosdqqx4FJF/qr/IvtrW6ixL6SBqqwOqt43auv0ePlXBx7iWmASEYFj\nDR00vB9RP9S6m1qbi1vhGaqtTj7SusswnNd5fuwGOzxeYpk0SSWLKMDO6np8UgSbZCGWTSOrCkPR\nABoafzn4Ns/0PYjXvvzFbi4Z5Y9uvkGV1YFdshDJpPidd3/Ab5/+Sf7r1RcZTbZRp4wR0dxomopd\nVKl2NOGyOplPxYhkU0X9LuVSjK9HIBAgkUgQjUYLLiJuFGuNkKPR6D1pTg+mIC+LpmkEAgEGBgZw\nOp0cPHgQTdNKntJRqv+FLsiFbCJjsRjf+c53GB4exu1286lPfYpsNluUGEN5KYtEImF0O+3cudPw\nF8idYNLQ0EBDQ4PRnqpH1svVtS4Wh5lEhNrsOF7JTigUQpMVIskoP7p0gVZHlSEKVVVVuN1uRFGk\n1VVNq2tp+ZEgCBypb2e/t2XZkU9DUT9oGh6rnR0eL+FMimA6iUO08K8PP8JvvvN97oTncVgsnKzv\n4npwml85902e6DrII237jNx1LJtmJOrHJllIyAueJfp7VtsczCWjfH3gEu/4J6l3niGZdaClbqEJ\nDtpqunBZq0nKWWyiBbdlY42MYKmvh9vtJhaL0dXVtewU643w9VhrPbSqqmuKsDeTe3OvF1Huj7ec\nWOoRscPhyPP/TaVSZZfKlSLIhUQzGo3yla98hVAohNfrRVEUnn/++ZKi9VK670RRJJ1O841vfINw\nOIwoily5coWPf/zj1NbWMjw8TFNTE6dPnyaTyXDnzh3jtfqiY7EpD6/dhctiI6EulDuJLjvNzgYe\nOnaG8UiAiXAAZyxITShEIpEgrcicy8wzpSbpqqrj03vvo8G9IM43gzM8O3KVlJJd8ITYeZwa293F\n1oScYTjqZzYZwSZKCAjsqPJSb3cR1jT21TbzLw98hD+9/RadnlqGI37mkzEUTeX1mSFuBGf5tUMP\nE0gn+NLV7xPPplE0jV3VDaiaRlZVsIoSsWwal9XGRCKMTbQgiCIZ+3Gi4gF6RIm0quBPxxEQ+NWD\nH0UAAnKKuWSURodnUxo09ONUFEWqqqqWOKbpU0JisRjj4+MkEol18/UoF731/l5lWwgylNecsNib\nQo+I7XY7+/fvXxJ5lmMIr5vUr2SMvtJ7JBIJBgcHCQaDJJNJmpubjYNdX7gpFt3j+Nq1awB0d3cv\nW+QuCAIzMzN5+bhoNMpzzz3H448/vsQUaS0ngV2y8Ezfg/zhjdeYyyZpw8PP7jrJS1N3eGXqjtEo\n8sSOgzzUd4L/8+qLvB2ZxYHE4Iyf6/MT/HztPjKSwLejQ9Q7PDS63EzFwvzd8Dt8bu/d9tm358ew\nSxZqbE7SikxGVdC0hcXDi4wC4LU7sbx/hzCRCGGVJJyClTZXDZOJMIMRH3986w0GIz5qbU4aHR7u\nROY53dTFxfkxBMAqSvxvh36My75x3vaNGd9PWsny4519PNy2l2A6QZu7BrfFxm9fe5l3AsP8zdvj\n9Hjq+cc9R+mubii6K7ESrFZlUWhKSK6vx2Lz+XJ9PdYi5vr3vBU6Dsth2whyOeiCHIvFGBwcxGq1\nFhTixc8vhXLn6ulGSeFwmF27drF3714uXrxo1GhqmmbcmhV7ixePx3nxxReNCNZut/PTP/3TeSeY\njiiKyLKMpmlks1mi0SiapuF2u5d0Hlaidbq7qo5/d/QxXn/3MjcdGb565wLXAlMcqmul1VWLrCo8\nN/Yeu2oauRqcos1diyAI1GlV+NJxmvt2EUslsPVPYkEgFAohpzNc9M1xRqmhumoh9RFJJ7GLEoe8\nbaQUGVlTsAkSN0OzXE7MYfGNc6SunQ+39PLD6QHSsoxdsnK04X1zfE3jkm+MG6EZLIJEKJMkmk1T\nbXPQXVXPZ3adJJRO0uSswmO101NVz83QDNeDM4DGAW8r/3jncVwWm1FZ8Y3By/RHfFSJNnzJOINh\nH6NRP/c1dfOzu08ZY6zWm3IW9XJ9PZqb79qEluvrsdZBr4lEYonHyL3EthHkciJkRVG4evUqTqdz\nxYkYue9RKqVG1ZqmMTw8bORsc42SPvKRj/DKK68Yz+3r66O2tpZYLMa1a9eIxWJ0dHTQ19dX8MS6\nffs26XSatrY2kskkoVCIl19+mX/0j/6RIaq6WIuiSE1NDel0mmQySXV1NYlEgp6eHtLpdJ5BUqVa\npyVB5K3IFIrgpN7uxiZJDER81NicuC0Lt8EZRQZtoZzt7q+xMGXD66rCardR7apBFASi2TQ1msaO\nzk6i0Sjz8/MIgRDRSBQ1mcZpsRHWsnR56vi/rr1MKBrmO2+PU2Nz8FDzLv7pvvu5MDfKZf84KjAV\nD9PsrGIkFmCH28tYPIhHspOQM6QVmW5PHXV2d15bt12y8Ev7HuBmaJYam4OTjTuQhPzfZjQWwGOx\nMaGEkUURj9WGJIpMxEO87RvnwebKdZ2tRCl3cqtRrq9HObMQcwmFQvfsgh5sI0EuhWAwyODgoCEw\nuUbYlaZYQdYd2GZnZ2ltbeXo0aNLDsyTJ0/S3NyMz+fD4/HQ29vLuXPneO6550gkEthsNkZHR4nH\n49x331LPhXQ6jSiK+P1+5ubmUFWVWCxGS0sLsixz8+ZNRFHk2LFjOBwONE3jqaee4tq1awwPD6Mo\nCiMjI3z961/niSeeMGo9K9k6PZWNcdDeiKJpuCQbkWySWDZFQs7Q6PCww13Hgy07+eH0IA7JQkaV\n2V/bwg6PF0kQub+phzfnhpEQsIgiP7fnNFWeu/nQPeyh3dfNX/a/TTSd5D57A+eC47g1EX82STqr\nEE4luCKNM5uK8utHHuFQXTt3wrN47S4+2rabP7jxOge9rWRUhdlklISSYbejkVuhGRodHlpyFhvf\nmB3ib4euICCQVmWu+Ce4r7GLvbXNxkJel6eOW6EZMqqKKEikFYUamxOHZCGcLt86slTWu1OvGF+P\n+fl5EokEFy5cwG6359VOF+PrcS+XvMEHTJBDoRADAwNIksSePXuYnZ1d1gqzUqyW5pBlmdHRUWZm\nZtixYwddXV24XK5lo4TOzs68C0g0GiUajRrTLxwOB++++y4nT55ccvD29PRw8+ZNAoGAkc+rr6/n\ntddew+VyUVdXRyQS4cUXX+Sxxx7D5XKxe/duJElidnYWr9eLIAjEYjFeffVVnnzySaCybm+1kp1w\nNkWtzcmx+nbOzY8Sl7P01TZzrL6Di75RPtGxn51VDQxE5ul0e/lE534j6vzxHQc4Wt9BQs7Q5PTk\nLejpHG3o5GjDwncYyiR57/y3CKWSRFIZbJKFpJplIDTHYGgOORDh8cY9fLimCY/Lg0XW+Gjrbp4b\ne4++2mZqbU5uh+dIKBn+YeImr0wP8G+PPUarq5q4nOHvht+hwe5G1lQu+sZ4LzjNZf84bc4afvXQ\nw1TbHPxE10EGw3PMRUJEsim6q+rpdNcyl4pt2HBT2LxOvVxfD31No6+vL28R0e/3502wXs7XIxwO\nmxHyVmCl25xwOMzAwACCILBnzx7jRw8EAiX7WUBpZTnLRci6A9vExIQx+VqSJEZHR0u24Cx27FFP\nTw/79u3j0qVLiKJIQ0MDtbW1zM/PY7fbCQQCRgdXIpEwRFs3uNG363A4CIVCee9XKbe3h6s6Oa/F\nmE5GUDWNZ/oe5GPte/nawEW+OXIVURABjad7T/CJzruetJqmMZ2MkFZk2lw12KW7J2UyG+WHQ1/F\nFx+jo+YAD3Z/2rDfrLE6aHRUcTs8h6gBCGhASEnjtbvIVDu5LEX5SVebUTctpFIcyjqYFjKk0yk6\nnNW0OKsRBIHZZIQfTvfzdO8JoxTOJlkYCM6gaBpOi40Guwd/OsGr0/080XUIl8XGv9z7IGeoY77G\nwq3QLMFMkkfb97G/tmXN32uxbIXGEL0ppBxfj7feeouxsTFSqRSJRGLNjVsvvPACn//851EUhV/8\nxV/kC1/4wlo/3qpsG0EuRK4Q79q1a8mVcy0WnMXm2vQqC51cB7aWlhbOnDmTt/pcas65vr6e+fl5\ngsGgMa33xIkTBU8sSZLo7e0lGo0Si8XweDz4/X6jVKiqqopUKkUymcTj8ZBKLTQr6It++i1tNBql\np6fH2G4lDeobLE5+7dAp5lIxXBYrTY4qhqJ+rgem6XAtLOSlFZlvDl/lRH3nQnSuafw/N1/nWyPX\nSMhZPFYbXzr1E5xo2IGsZvnzt3+ZufgwIhJDgUvMRPv5J0f+s5Ev/8yuk9wKzaJkZTJoyKqKIghk\nVJX5VAyXxYa9torW5gVxHAjP8z/unCeRTjGZjOBCwppeyG0nkJn1+/DX+nG5XHhtLnypOCkli6qp\n2EQLDslKWpHzmkFEBJrtbh7uPYCiqQgIq3pcVJqt4Pa22j6s5utx48YNbt26xSOPPEIqleL55583\nDOZL3Y9/8S/+BS+++KIxBf6JJ55YN2N6nW0jyLlRYSQSYWBgAE3TCgqxji5gpbBS48Zyz9fLwvTJ\nIbkObMs9v1hsNhs/9mM/xujoqLGot3v37mX3BeBTn/oU3/72t5mYmKC+vp6f//mf57nnnuPWrVvG\n8/S/wYLf9NmzZzl//jwAzc3NfPjDHza2u9xw0lJFWv8N3VY7PTmtymlFRhLvGirZ3ndgUzQNiyBw\nyTfG3w2/g6Zp1NgcxLJpfuPt5/mrj/4c0eQg/sQ4dslt7FO/7xyxTIAq+8Ln213dyIMtO+kfH2VS\nyjIZD1Jjd9LjqSeUTeJJx7G+X36WUrJ8beAiLquNRlcViiRw2TdBY7UXBLBn09xXv4NAIIB/dJjj\nKQevxgOks2myisyBumZkVSGpZDlQe1cocqPTxYt+G8VWipBLxWq18uEPf5ibN29y//3388wzz6wp\nJ37hwgV27dpl2Hg+/fTTfOc73zEFuRRyhbi3t3fV5P56mdTnIooi4XCYt956q6AD22LKmRpis9kK\nLuItRh91f+vWLc6cOcPOnTux2+0oikIymcTr9WKxWLBYLLz77rscO3bMeO2xY8c4cOAAsiwvKVWq\nVM3nciLe4a7FLloXpnpYbMynYxz0tmJ5/2SbSoRJKlm8toVbVKfFSlzOMJUIU8wweLtk4XN7z/KH\nc0FCQoSe6nrQICanScpZ9tY2k5SzvD4zSCSTIppNUe9YEPMWZzUeq42pZJgWZzW/fPhh9tU28//1\nX+B6ehpBEHhk1wF+zd3C9ydu8SPfCJFwhDPuFmyzYcaTGh6PZ0tM69gKEXIlfCz0NZa1fJbJycm8\ntZqOjg4jIFlPto0g625nu3btKnqVtVKeyIXIdWCTJKmoEU6wfnP1QqEQt2/fJpVKcfr0aSO/NjU1\nxeTkJOl0mtra2ry5cZlMJm8bNpttQz0NdKptDp7pe4BnR64STCc41djFT+w4aPx9h8eLiICsqkiC\nQFpRcFqsuCw22lx7qXO144uNIggSGiq76u/DY8tfLKu1OXm4qpPjbdW8NHkbmygSlTM0aypnm7r5\nNxe+S0LOoKIyn4zjttiptTs5NzeCommcqG8nlknzXnCagcg87wamaHPVoGgaL0zdpmNvHQ/vOcwj\ne48utHxrGrfnJ/n9/gvMJ6LsEN18yNZIOp3OaxVfyR2v0mwFP2RFUdZ0jJlVFlsEfYGqFNYrQg4E\nAvT39xtVCoFAoCgxhrvOaauRyWSwWq2r+lPEYjH6+/vRNI29e/dy+/ZtQ4xv3LjBa6+9hqZpZDIZ\n5ubmaGpqMoyBVqvLrjR6fljR1Lzb9uGon/7wPMfrOznW0LHE9+FoXQdPdh/mmyMLHYgOycqTXYfp\n9tQhCAI/d+L3eHXwz5iPj9JZe5AP9fzMsiL3cNseVDSu+MZpdFbxic79fG/iFhlVNsrZsorKrfAs\nTQ4PcSXDyfpOaqxOqq0OLvkmqLM78doXKmUswkIu+L+998OFOwDgSF0bn+49ye/cfp2spuJyO3kv\nHkZD4n/fe8aoLJifn1+Y+ygI3NaiDGejNHqq+cmdx2jxVL6SYCv4Ia91fFMkEqmIILe3tzM+Pm78\n/8TEhOHVsp5sG0HeCmOc9LI6i8XCgQMHFjrDIhHm5+crsn1Y8Nn4y7/8S/x+P3a7nQceeKBgZ2Ei\nkeCll16iv78fj8fDmTNnqKqqMsRbVVXeeOMNqqursVgsWK1W+vv7CQQCeL1ennzySaanp4ve77US\nlzN8rf8Cb/lv8e0LMzzZfZj7m3fybmCKvxx4G4soIqsqF+dH+V/6HsCVI8qCIPDrhx/hJ3YcYiwW\npM1dzYHau1OiXbYaPtn3yyu+v6ZppLUoiUyAj7fv47GOu92Iz45cNXLIsBCxH/a2cn/LTv7s9nmG\no35uhGawiwKZzDjz2iwR6jncdAyXtYaRaBCrKLK7ZqH29rJvAosgkVJkGp0Lv12D3c27sTksNpth\n1qTznZGrvDA4ih2Jm5E5zk8O8lnvPhrc1WUZ0S/HVsghrzVtEolEKlL2durUKfr7+xkeHqa9vZ2v\nf/3r/NVf/dWat7sapiBXIEKORqP09/cD5JXVQWVTEJqm8bWvfY1QKITD4UCWZV5++WWampqMleRM\nJsPQ0BA3btxgZGTEWJh77bXXjGYPWDjw9YM/FAoxOzuLzWajpaWFhx9+mJ6enrIFOZVKGa54ukmN\n0+lc8WT/5sg73A7PUS85qLU5+ZuhK7Q4q/nexE28Nhdu64IAj8dD3AnP500A0dlX22zM1iuGQDrO\n9yduMZeM4Au/CeHXOXfOxo7awzx56IvYpIUa5geaenjHN4FNtKChkVZlPty6i77aVqJymlAmiV2y\nMBadpE6MccQr82YkweWZi3TXHcdleX8u3/vHqF2yEEwnUHOMcOT3TYmkAsfx9ydv0+rxYntfqGYS\nEZw9bfRWNRt+JtPT06RSqTWNddoKeexK5JArYU5vsVj4/d//fT7+8Y+jKAq/8Au/wIEDB9a83VXf\nd93fYQtTih2lTq4gx2IxBgYGyGaz7N69u+Ct0uKyN1hYMHj77bfRNI2TJ0/S0dFh/K2QIGcyGc6d\nO8fw8DBTU1PU1NQgCIKRA5+fn2f37t1Gg0lPTw92u52qqiojH2e3240BrLCQP+/q6mJoaAifz2dM\n92hububixYu0t7czMzPDlStXaGtry/MpWI5sNsvw8DA+n4+uri5EUcwr6hcEIS8/6vF4DLG4E5qn\nwe4mQAKbtFCHOp2IkFEV7OLdw1QUFsRrraSULH89eIm0IhOKD/BeYAqH2k2bAhOzw7gcf8FP7Pun\nAHyopZekkuX5sfcQBPh07wlONXYxmQizq7oeRasjmokhpoPEVTeX4yI1VoU6cY6f7ulhICHy4uRt\nqqx2NBaqRu5r6iIupzk/N0pGUxA1eLrlQEFBFBBYaBbPeWwZI/pCY51yHdn0773QkNTNFmOozLSQ\nSuWQP/nJT67bqKbl2DaCXO5QzVKxWCzEYjHeffddEokEu3btWnHk+GJzoYmJCf78z//cEN3r16/z\n2c9+lh07dhjPXyzIL730kuHLrCgKwWCQ+vp6stksyWSSK1eu4Pf7OXbsGGfPnkUURW7evEkoFCKV\nSuF2uwvm5h5++GFjNqDL5aKzsxOHw0E8Hue73/0uIyMjTE5OIooiH/vYx5Ytp1NVlUwmw/nz5+nq\n6uLMmTPGgNfc70ZRFEMspqenicViqKqKy+XCmpbxqwl4P2pUNY0qm53TTV18b+IW9TYXKUXGJlro\nqVr7iHd/Kk4sm6HVVc1U0IdN1BjJ1JFJZ1A1B98aH+PBnoSRC/54Rx8f78g3VUqmZ7kz/xaSGqTa\nXkdCEQmoNqyKhipbmdXqaHTWcbipi4lYiNvhORDg/uYePtKym2v+SXqrG7CKEmpWZiIVIaVkl5gJ\n/fiOA3xj6DIui42MouC1uzjgLVxbW2isU64jWzAYZHx83FiDyL1AVqLbcq2sNULOdW+8F9k2ggyV\nbVAoRCqVYnJykkgkwsGDB2loaFhV1BcL7Llz51BV1XCkSiQSvPXWW8sKcjabZXBw0KiA6OrqMmqO\nk8kkDoeDTCbD5OQkBw8eNFzahoaGDL8K/XXHjh3j+vXrxrbtdjsf+9jHSCQSKIqCy+UiHo+TyWRI\nJpO43W68Xi+ZTIbXX3+duro6Ll++TCaTYc+ePfT29jI3N8fAwADz8/Ps2bNnxYVGSZKWTK3QxeJT\n8w7+dOgCwUwC/8QIe911OAIJ9lfVoDbt4nbcT7Ormkfb91G/xunPsGCPqacMPDYvQXkO0PBIWWQl\niUXq4B3/JB9tK3wRSmVjfOfdf0OtZmNKaSYSDxBQG6gR41jJggCitY2ZtESv18a/PvIIvlQMSRCp\ns7sIZhKEsimO1i/cHcViMWaSEfypBO3u/BzoYx191NgcXPVPUmt38ljHfjxFjJTSWc6RLbc1eWxs\njGQyycWLF5fM3ttIgZNluewI+V72QdbZVoJcLqvlzvS8bCAQoKmpCYfDkWeOshKLt6soypIaXkVR\n0DSNmzdvcuPGDXw+H7t27aKxsRFRFA2vCEmSqK2tJZFIGEMrm5qajEaVa9euEQ6HuXXrFmNjYzQ1\nNZHNZslmsyiKUtD72GKxcP/99/Paa6/h8/moqqri6NGjXLhwwejgs1qthMNhvvnNbxoRzJ07d+jq\n6mLPnj1EIhFGRkaMFuv777/fKKhfDV0sjrh38V/aO/je+Tc4cfgILTYP8ffzoy0xlZqkA0mSiE/O\nMuWJG2JR7iJUo8PDkfq2hQU2exeaOEuV6EdRs7htXjq8fWTU5dcXZqL9ZOQkvU6FJmWatCqRTthp\nq+okK4ewS25Ea6vRbScKAk3Ou1UrehScURVsooSsKmiAK2fcVFLO8tU757jkH6fK6uAX9pzhUF1b\nWZ+3ELn+xpqmEY/HOXHiRMFpIbmz93Sjn/VIcVRiYXErpF7K5QMvyCu1QusObHNzc3R3d7N3715i\nsRjDw8Nlv9/Jkye5c+eO0ZasaRqnTp3i2rVrvPzyy9hsNoLBIN/4xjf4zGc+g9fr5dSpU5w7d84Y\ntNrY2MjevXu5deuWsf+KotDf38/AwIBxe6qPSlJV1ehI1O09I5EIXq+XwcFBrly5Aix04D322GPI\nssylS5cIhUIEg0HS6bSxndraWsLhsLEv9fX1vP3227jdburq6pBlmQsXLuS1VgPGvDZ9XFAhqqwO\ndtiq6Kle+Ltz0XN1j91oNJqXH9UjOj0/WkwX5cJA1P3srm4ilk1xur6Lv3nvh3Q1NeO01pBQ5BV9\nJGwWJ6qmIGoaVVIat6jSJQ0zGrNhFUHRMlRbY+yufhyAaDbFhckfMup/k3qHi/u7/gmP7zjAd0ff\nRUAglo7zYF1X3ty+P7tzjjfnhmm0e0jJWX7n3R/wX04+Tru78nW2elBSaFqIXhapt9zrjmz6xVT/\n3gtNsi6HcgU1lUrhdC41k7qX2FaCvJapIbknsSzLjI2NMT09TWdnp5GXhfKmhuTS29vL008/zZtv\nvgnAmTNn2L17N1/5yldwOp3Y7XaSySTpdJqBgQFOnTrF/v378fv9hEIhzpw5Y0Qxg4ODRKNRUqkU\ngt7VnP8AACAASURBVCAgyzL19fXY7Xai0SiRSASPx0MikaCvrw9N07h9+zYXLlwwUhOCINDR0UE6\nnWZsbIzvf//7PPnkkzzyyCP86Z/+KRaLhcbGhYaFmZmZhfbk98XZarUiy7LhCZH7/aiqapzkg4OD\nxpQSgH379i0xuS9ERpF5afI2E4kQ3Z56Ptq2e4nHrqqqedaNw8PDyLJsGKHrYlGoJEwUBKMUTVEU\nQuOzxDwuJEHkwZaddHqWX61vqdpDt/co/f4LqOpCQ8UOu5V2W5I52YldVGiVxgnGbxPO7uL/vvos\no8F30RDptt3izvyb/MJ9f8D/uv/D+NNxkoEwna58ob3kG6fJ7kESRTyinZicZiDiWxdBXmkxLdfo\nJ/cCqdtmRqNRZmdnGRwczDOhX+m7Xw9CodCyE3DuFbaVIJdDbtWEqqqMjY0xOTlJe3u74cC23PNL\nITctsnv37iULZIsPWE3TmJmZ4Xvf+x52u52zZ8/mLdTU1tby1FNP8corr3Dx4kVEUSSZTOJ0Oo1c\n4ezsLMlkkj179vD4448b7li5YqqnP5LJBd/dS5cuGdadXq+X1tZWEomEMU9QEATS6TTZbJYHHniA\nhoYGbDYbgUDAiKJ6e3uNxcxMJsP169epqakx5gXevn0br9eLz+dDEAQ6OzuNE0n/HlRN4w9uvM71\n4DQOi5VzcyMMRnw80/dA3neVG9HppX+5RuiLS8J0kaiqqsrz19U0jZ2OGk7sPVHU73l16nsM+C+Q\nzsZQtCweex2pbIRWV4Cu94U1mlbJKFm+NXiJQOwOVRYFEJiQ26nJ3OL6zA/4yM7P0u6uZSy2tEuu\nymonpWRxi/aF9BHk1V9XknJSBbm2mTrLffe5I530YbWLz621lt3d6116sM0EudyqiUwmw/j4uOHA\ndvr06WVvvcoRZN0ic6X9O336NC+88ALZbJZEIkE2m+XGjRvU1tbicrmMiDIej/PKK6/g8/lob2/n\n9u3bWK1WqqurSafTTE5O0tHRgaZpnD17lqeeesp430gkQjKZxGq1GqOgVFXF7/cbC3FVVVW8+OKL\nPProoyiKYtye6oKWSqXo6uriyJEjdHd3A/DYY4/x7LPPomkafX19nDhxV9T070o/+fSW7Jdeesn4\njm/evMmjjz5KTU2NcYcznYhwIzRDq2vB1lKzObnsHyeQTqy6qLecEXruItbo6KhRiud2u7kW+SbX\nfM/z7Regq/Yw9+14in3NDxq1yLmEkjN858Z/RVZlVBZENpGJ4LLWMB25TUfNAWQtjcPiprFqD0n5\nIjZRI6MslO2BRkbLT6tomrZEEH9+z2l+7/qrROU0mgb7vc0F668rQaV8LJb77nNHOunpJr3CplJ5\n6XvdCxm2mSCXin41v379Om1tbcs6sOVSznQMPVpcabVad5G6fPky2WwWi8VCT08PgiAYExQ+/vGP\n89WvfhWfzwcsjGSSZZnq6mpjsVBRFGZnZzl+/DhPPPFE3gEuyzLJZNIYmwN30zx6VUQ6nWZiYoKR\nkREaGhrw+XzE43GjblkURSRJMsQYwOv1cvjwYY4fP26IrC7EDoeDqqqqvPRJNBo1Kjhg4US6c+cO\np06dMrapUfg7Xu7xxQST07xw67/hS4zR4NrBY/v+FV5na96QTkWVeWP467w6+k1mEv2AALLKLd+P\nGAm8S7vnIE/u+3d4a+rz6nb9iXFEJERBfv87FFE0mbScQBQkap3NVDuaeLDnM9Q4mvA6nERde5iL\nXCKrLkT/1VaRA80PGftbaJbc0foO/vPJx+kPz+O22jhW35HXMVhJ1rtLb7mRTrq3cTgcZnx8nHg8\nzpUrV5Y0txSzb6FQyIyQ70U0TWNubo7BwUEkSaKnp8coO1sPVss7h8Nhvv71rzM1NYXH4zHydPoJ\narVaSaVSTE1NEQwG0TSNZDK50O6bThuRh6Zp2Gw22tvbmZubW3IReOmll7DZbMbwUk3TjChZkiQj\nD51Opzly5Aher5eBgQGGhoZoaGgwSuwKTbpePDUk16TozJkzXL58mUAgQE1NDd3d3XkG93qpXu7r\nWpzV9FYvTAVxWWzEsxkO17VRb1+95C2rpHn22n8gngnjslUzFxvh2Wv/kZ8/+WXDmF7TNL5x9d9y\ne+514tkwC40Xdyf1KaSIZKe5Mf06dVN7yWQy2Gw2PB4PitWKrKQXXqGBxsK+Z9UUoiDSVr2Xj+76\nnLE/P7vrFF/rB1kTiaQmecgr8dS+X6HBffeYW+4OqsNdS8c65IwXsxlOb/rdiZ5ii8fjDA8Ps2fP\n/9/ed8e3VZ/rP0dbsuUZT3nF20nsJHYcQhogFAKU0ZRC4bbQci9QbnuZhVBW4ce4gctO2FBWC5RS\nuGWlNJeyVxInZCfee8jb2tKRdM75/aF8vzmSJVvLluPo+Xz44Njy0Sv56Dnved/nfd5yek4TkgYw\nrRwvXrKYY5judoc4sLW3t0Or1aK2thYjIyMzrl/0N61H4tHr9XjrrbdgtVqRk5MDl8uF7u5u5OTk\nwG63QyaTwWg0oqamBgBoSUMmk1EjIofDQT/Q6enpSExMhNlspqb1RqMRSUlJMJlMUCqVSE5OhtPp\nhMPhgFwupyoMjuOgVCqRl5cHhUJBV13p9Xq63dpsNmPJkiWTXou4oSoIAjiOo5pSlUqFE088EYcP\nH8bAwADNjKRSKd1qTTJucgyZRILrFp+Cf/QcQq91AkXadJydvyioW1qjYwhWpxGJSk8GnqhMhYWd\ngMExSEnQxI6geeQbSBg5JJAcKT0AhJQZRgq5TI70jFQszV0GwHP3MGYYwketrwKCFA7OQPN1BhJI\nGBnUci0aet/1IuQsdRJurP4hrO4fQCWV+81yY+0jEevnB0DLaGI5HgHZFGI2m73keOQi2dTUhPb2\n9rDM6KfCzTffjA8//BAKhQIlJSV45ZVXZpT05xUhT4Xx8XG0tbVBpVKhpqaGTq3JZDLa0AoFoa5x\nEtedxdacWq0WLpcLGRkZtJtNiNBgMMDlcmHJkiVYunQpPvvsM1itVqotlsvltBzAMAykUiksFgsM\nBgMkEglGR0fxzDPP0My1oqICAwMDdEwaALKzs9HT0wOFQgGlUkkldUqlZ8tzZmYmTj75ZGzbtg0c\nx6GsrAyrVq2a9BpJhkzIWBAESrjEyKipqQlKpZJ+kNRqNaRSKerr6/1+kDQyBX5WvHzS96eDUqaB\nAN4jS2Ok4AUOAngoZUclZRzvBsmGbciC3p0ACThkSfVQMXYkqTIglciRl3LUkFypVGLfxLswcD3Q\npZTDyTswYumCy81CKUsEeIBzu8FyThw6dMhLDqZQKKCVB3b8G2It+Ed3N1x9DOozCnBydsms6mnn\nuheyv00hRI5nsVjQ0tKCTz/9FMPDw3j99ddRU1OD559/PuKLzLp16/DAAw9AJpPhlltuwQMPPIAH\nH3wwomNOhXlFyP5OYKPRiNbWVshkMixatGiSM1okjm/Bai7FJYuJiQm0trZCrVZj2bJlUKlU+Oyz\nzyhJkVJCdnY2Tj31VHqMvr4+7N+/H4WFhejt7aU+zmQbCdk0wjAMRkZGUF1djTfffNOredfU1IT8\n/HwMDw97VAXFxVi3bh2kUin27dsHp9MJnU6HmpoaqknleR6LFy+msrmppFFut5sqMaRSKX1/WJZF\na2srUlJS6AfEYDBg0aJFyM7OhiAIcLvdUcvQtMoFqMs7D7v6PvDUFBgGK/LWQ6s8KtlKUWcjXZOP\n/WP9aOeKIIMbAmSwMYVYlTSEHK0Op5VeiXRNntex+4yHoZYlei6eUjU4aQ4GXQIUbhcypQbIZFKc\nUXYVChcUTsrmiL8IURoQOdiow4IX+/dCqpBBo1ThsEEPh9uFM/OnlwZGC3MlQw7loiCW4914440w\nGAxYt24d1q5di5aWlqi8njPOOIN+vWrVKrzzzjsRH3MqzCtCFoM4sAmCMMmBTQwi/woF4RAy6ewD\nno26YuH92WefjQ8++AAsy0IQBBQUFCA723sowWw2g2EYmlkSSRmpB5OmE6l1Dg8PU2UE4MkwJBIJ\nCgoKsGTJErrhmpy0vs9HfoeUEAKd3CQDTk9Px/79+6mBEJGiabVa+rskBvI12U5CsuqhoSHwPE8v\nNkTfTC4OoWBN0SUoSKmBwT6IFHU2ClKqwfEubO95Bx1j30MjT4bJMYxhIQ9Kxg0544YUDAoz1mB1\n4TKcpqugx5qw62F2jCJNo0OqOhc9hgOQS5Vos6nwlbkCjCQRvMAhl3Hj9iUnoj7P8yH2zeZYlqVb\nwr9p+xt6rLugkKghUa+FxeVAoTodKrkKSokMnww0zyohz/UMORiQGrJGo8GyZcuiGJkHL7/8Mi6+\n+OKoH1eMeUXIDMPAarWira0NTqcTpaWl01rxyWSysLeGKJXT+wnY7XaMjIyA4zgsWbLEbzylpaX4\n93//dwwPD1M/CV8vCNLoGx8fp7VfjUYDu91O68gAaHadkJCA7u5uepITolOpVFi0aJGXJGl4eBjf\nfvst7HY7SktLUVdXR5t8gQxnCBGTn+t0OuTl5YHneVgsFphMJgwODqK1tRU8zyM5ORl6vR4ajQaC\nIGDBggXIzs6GTCaDyWSidw21tbW0ri1Wf5AyCBkln46kGYZBYWoNClNr6Pe+bP8TDug/gVqehDFr\nL8zsKOSSAghQQsF4ph1Zt9VLx7G9+x182fEqJIzn/VhX+hv0GA6i19iGb631YBglcjRH/jasDc32\nVNRjMhiGgUqlgkqlQrvtS7Q7/wW5Sg07b0aP+Z9w88s9E5ZWKxy8GyqZAr29vfTiFo0JuKlwLGbI\nvgi3qXf66adjcHBw0vc3btyI9evX069lMhkuueSSsOMLBvOKkJ1OT+2upKRkSgc2MWZqa4jT6UR7\nezsMBgOSk5Oh1WqnvDiImxitra2TmoAZGRk466yz8Le//Q0cx0GtViM7Oxvt7e1ezTSpVErr0AsX\nLkRHRwc91tlnnw2dTudV2iEeFcSzYtu2bXC73Vi9erVfkyBCkqS0Ip7SAzyZr79hgaqqKuzZswd9\nfX2QyWTIysqiZRLAMyyTnp7uNfFHICZnX5IWP+9UJC0IAg4PfQmtagEkjBRSiRwD5iZkS/Voc5WC\nYwC3IIVGrkbNEb+IUWsPvux4FRAYmNgR8IIb7+y/FxKJFA4ecAsMJAwLATwkjBQyiQT91glY2HH0\nGg7ii45XwLqtKElfiTPK/4vWsPcO/BNKWSLkUs8FfQE3gUG3AzaFBGq5ChK3Cz/NXwqpVIqRkRH6\nN/S10CR1/mgg0tVJ0UCk20LC9UL+5JNPpvz5q6++ii1btuDTTz+d8br+vCLkYJd9ihFuDTnQ77jd\nbnR1dWFoaAgLFy5EZWUlBgYGQsrCA8nkFi9ejB//+Md47733qHSPkBPJgonFJcdxsNlsqKiowA9+\n8APk5eUhLS2NZqwEfX19cDqd9ESWSqU4ePAgVq9ePUk5ARzNVEMpIzAMg+TkZKxdu5Yeo7OzE8PD\nw7SZ2dvbi9bWVigUCiQlJdFyh3iabiqSJl+T941cnMR6a5lEAY53QyKVQiqRIVmVDaXbgEpJJ4bd\nKchSFuP3S8+hJkBGxzA43g2jYwgCBDBgwAk2KJGAZHkKEqVOjLvlsDmNkMkSwfFuTBi/xMs7X8WQ\nuR2p6hxoVQvQMvIdJIwUJy38JQABUkYOXrDS16JkOJyXnABmQRmcjIDlC/KoCxwBkToazCYcHOqB\nucOKFF6GRKXKqy493SKAQJgLGXIkTm9A9NY3ibF161Y89NBD+PLLLyO6WASLeUXI4Vy9wvGm8Cdj\n43kevb296OvrQ15enpf/BZlwi0ZM/f39tLtMiFWpVNIMNykpCXK5HBaLBTU1NTj99NO9Mh/frNf3\nQyiujZPHissT4rpuqCAyv+7ubjqa7vv84jrr0NAQlfhptVpK1OJBAalUCpvTiMbhr+DkWJQuWIlU\nVe6kbBoAVuZdiK0tm2FiRyAIPBYkFOIXyx6AwTGIRFkG7ENKujePFzjIJHLY3WaaAQuC51hu3gmG\nAVZpWvGtpRQmdyKSpUCF2ogCxRhMDiOcnAPD1k7w4KFVpKOh913sGfgIAJCuzoeLs8HNs+AFDonK\nVJSpl2Jp0dKAuxcZhoFMqcA7nc1oN40AYJCm1OA3pSdC5vRchEdHR6npT6BFAIEwF2rIofRl/MHp\ndAa9uzJYXHPNNWBZFuvWrQPgaew999xzUX0OMeYVIQOhGwxFuvqJkExnZ2fAseupMmp/IJ4PvuA4\nDi0tLcjPz6eZant7O23kORwOsCyLCy64AFVVVX5fmy8hFxcXIyUlBRMTE/RnpLNMmoREvREuEQNH\n1SXJyclYsWJFwIlIfyY2LpeLkjTxgmYYzxJWqYrDe513wu42ABDwaZsK/1G/GbrkSvr75GKSoEyG\nxTkGXvCMOxvserSM7MBppb+G3WHHp9Y30HP4X0hSZeLw0OcYs/XBzXlI0/M7AMBAAA+Od0EOB05L\nOoBL6/4LSeocvLX7egg8D4NdDwE8BAEYs/bCyhrAcha66XrU1oOqrJORpMyAUqbB8txz0NncP+17\nu224C82GIeQleLyxh2xmbNW34LLylUEvAhBn0+IL9VzJkMMl5JmaJWhra5uR4wbCvCPk2QBpBJJp\nP2KRGagGF85ePX8lDrEUjZQoSDxkqIOoNwJ9uH0JWalU4mc/+xkOHz4Mh8OBgoIC5OXlgeM4aLVa\ntLa2Qq/XQ6lU0gw1KSkpaAcvm81G9w0uXryYGvOHArlcPmlQgJDOJy1/hNE+DDmjBgSPcfy7ux/G\nL5Y+DK1WSw3zJRIJDg1/Cl7goZB6sihe4LB/8P+wduHleGnH1ei374EwduS9BgMP+R79uzHwEJZG\nlowk1QKo5Uk4q+I6FKaWQxAEaJWp6BzfAwZSSBkenMBBgAAHZ0aCPOVofZyRweY04MLqu+ixeb53\nWkIcYy1QHllvBQAJcgVGHJOnJqdaBEAGhnp6erx8ji0WC/USiZWfcDSy9GPZCxmYh4Qc7taQUE5E\nMsaclpaGZcuWTevBGmqdWiqVYnx8HNu3b8fExASKiopw2mmnQalUYs2aNfjyyy9p9iqVSqHVapGe\nng6pVAqTyTTlSe2vUadWq1FXV+dVhxUEAYmJiaitrfWSbJlMJuj1etjtds+ww5Fab1JSkpc5DNmv\nZzAYUFpa6kWm0QAhHblGgFwmh1quBiDA6WbACh4rzvb2dtoo0mq1EFyyI0TrgSAIkAoKfLLzbfTZ\n94iOLhzxzPA9HxhoFemQSmW44aS/ef+EYbCu/Ld4ccdvIICDhJFDJfNcHJNV2bC5Juh5yQtuZCQU\nef1+MOdfceICfMq1ws3zkDAMDE47VmYUBvV+ibeGiJ+TjMKPjY2hv78fXV1d9JwSjynPRvYcSYbM\nsmzMm5LRwLwj5HAQrK6YaJtZlsWCBQuC3kIbaobMsiw+/vhjKnzfu3cvzGYzLr74YtTX10OlUmHf\nvn1ITExERkYGPvnkE6pTzszMnGQOL4ZEIvGbfYsn7HwbdmLJlq97GiFp4gpHGmk2mw06nY5K6GYK\nFRk/wJ6+jzx1XUjAw41l+aejoqyCvi673Q6TyYQyx2nYi4/gdFshHGmuLUm4AJIkM2A4khUzDASB\nAy8wYCDgKEcykEnkcPNOZCYupO+huHmYmVCMS2sfxht7bgHrtsHNOyGXqnDeopvwRccrGLF0AQyD\njMQiZCYW443dv4dcqsZJCy8JqmSwNF2HcwsWY2tfIwQIqF9QgLPywtcqiwcr9Ho9SktLqcY9WosA\nQkEkGfJ8cHoD4oQM4GgGG4iQ7XY7Wltb4XA4UF5eDp7nMTQ0FPTxQyXkkZEROJ1OWheUyWTo6Oig\nq5JYlsWPf/xjegLabDYAHulcdXX1lJmCb4bsqycOpU6sUCiQnp5O4xwdHUVraysSExORnp4Oq9VK\nvZoTExNpySOYJlOwqMhcjbOrrsdnbS+D4104oeCnWFvyH/TnDHN0O3N2djZ0uX/Fv/b/CQ6XDcvy\nzkSKLB+Hh74EAwkEcBB4Ccb4TFgFLRgAqcwItFITPFkzD40iGRcu/X84MPwxtnf/DYIgoC7vJ6jN\nORcMw0CnXYJTF16Bf7U9C6lUDqUsAZ+2vYhfLP8fmNhhAMCQuR3/bN4MBgx4gUfH2E6sVP922ved\nYRj8KH8RTtNVQBAEKKXR+/iKLwj+nNmmWgQgzqYjMaOPpFwyH4yFgHlIyJE26cQQa4lLS0vpUlOT\nyRRSCSKQuVAgKJVKr40bZCrvwIEDKC8vn7RcNScnB4WFhUFtS/CnnABCI2JfEC8BhUKB5cuXT+p0\ncxxHm3J9fX2wWCwAQLMtQtThknR9wU9QX/CTKR/D8zy6u7sxNDSEM6p+4/UeFi0shH7HDrSObseI\nKwVWIQWJDOAWXBgTsiEX3EhXqJGhKcLlJzyJHuM+fNL6HBRSDQAGX3W8gkRlEqpz1nn0ziOfI02j\ng/yIl/KIpRMvbP81klQZWF3wb9jR87+QMjLIj9SybU4DetnvwTDnBfV6FTNgwTlddhqtRQAzBZPJ\ndMxvCwHmISGHA7lc7nUb709LLCarUFUTxJUtWOTn5yMtLQ1ms5kaCa1atYpqg30RSgYu9pwg/w6X\niMkFy2q1oqysLOAtI1nO6ptxkYk+vV6PlpYWuo1brEOO9LZY7PCXlZWFlStXTiIHqUSGK054Gk3D\n3+Bf/Z3ISihAiiYdvRMHsX9oH6SsExLGDoNtCH/55j64BDucLicsMMDNOyCRSLGnfyukEjk6x/dg\nzNYLjSIVEokEFscEzM5RsJwdNpcRfz/031DLk6h22gMGQdo8zxjCUVmEswgg2vv3COaDFzIwDwk5\nkgxZvMLJd5ee7+NDIdhQY5LL5Vi+fDn6+vogl8tRXV2NioqKoJUT/kAyYo1Gg46ODuzYsQMajcZL\nORFsU4S8T4ODg34vWMHA30QfsVgkNWmyo4005UiswcZptVrR0tICuVxOjZwCQSqRYXH2WowJOozY\nLVBK1chPWYLv+r7AQs0CJErsMLHDaHd8hgRFGizcOHjB4xgn8CwO6D9D6/AOMIwEboGFwT6MnKQy\nTNj7ATDQyLWQSmRg3Tao5Vqwbiucbit4gYcEcuQpa72SgukmD6ONaMre/NlnBtq/RzaGkGZjuGWL\neMliHoGMqLa0tEy7wgkIf69eMBgbG6PZ4oUXXhgU+UyXIYsbdiqVCvX19dST2Gw2Y3x8HN3d3XA6\nnVCr1V4kLfbrINOBRHNdX18f1YZdIItFsQ9uV1cXjVNM0uIxYrfbjY6ODhgMBpSXl4f0QV2eno/P\nBlowZDfD5jJDwY/AZOmEARwAHlJGAU5wwy2wACRHdBgMeLgglcqhkKrBudWwuw0wm81wcnbw4GB0\nDEMpS4Cbc0ImUWD9klvwbcdfYbPaUZ91MVYvPofqz4OZPIy1ZjhUBNq/RzaGTExMwOFwYOfOndTj\nmGTTwax2ihPyHEUoV1dBEDAyMoLe3l4kJCRMqSUWI5iMNFSYzWa0tLRAKpWiuroaBw4cCDoTDBTP\nVHVi8bYG4vRGFAlmsxkGgwE9PT10+kmhUFCj++XLlwdlrBQNBIrT4XDAZDLR1T8sy1JStlgsyMvL\no8taQ4FWocKP8hdhwmnHoLEZjULXEfN6z3vICy5olemwsGOQMLIjWTIACLA6x2GXSMHxLo8ZkcKN\nRD4dZucoOMEFm8uzJaVzbA/6xlsAeKYrvx59GhWOxchJKp80Hk7+H8jHIxJHPDFiod8V/23JTsjq\n6mq6BcdsNnupd6Zakmo0GlFcXDzrryHamHeEHCwmJibQ0tICjUaDoqIiamEZDKLZRXY4HGhra4PN\nZqPZnHd9cXr4ZsjhNuzEioSsrCwAHoVJc3Mz9QlwOBzYvXs3HRQhGepsrXoncZLaJYnTYDCgubkZ\nSqUS2dnZMBqN2LFjx5Ra6UBQSGXIUmsxMD4MtVwLp9sGJ28nT37U10NwiqMCDzf4I8b3vMDB4BhA\nblKFR1du6wcAyCUqcLwbDmECGmkqBB6w2k3407e34ScLN9I4xePhvkQ7ndlSOCQ901tzpoO4qUik\neOLpQ7fbTUseYimeRqPB559/jvb2dpSUlMxIbI8++ig2bNiAkZERrwnSmcBxR8gkE5VIJFi8eDES\nExMxPDwMo9E4o89Lslhy0pHb6tHRUZSWllKTHSB0wp8J5QRpbJL4xCciGRQxmUz0A+JwOOikICFp\ntVo94yTNsiza2trAsiz9e4oRSCsdyBtDjHRNvof8FcmQcQrYXWZAEKA3e7JbCWTgwYGBx7jIxTvo\n10pZAmxOIxwuy5GGHQNA8PjlHyl0yGQyyKQKSHkJeKkNWVlZMJlMXuPhYrkgUaJMZ7YkPgeinUnP\nFKYbCpHJZH6nD202G9RqNdrb2/HYY4/hoYceQn19PV588cWoxNXb24uPP/54RnduijHvCDkQAdhs\nNvrBLSsr86o3+aosZgKkEUiczfr6+lBQUODXYCdUkGEP8WBHJFn8wMAAenp6kJeX51eVIB4UyczM\npN8nJO1vmo+QSqSr3gnEjcXi4mKvC5oYvlppwL83hj+tdEFqNU4u/hW+6vgz5FIV3LwLgsBDgAA3\n76RkzMCTNTNgIJeqoZYneu64ZGq4eTdcLhZkV5+n9kwm9o6svOJdKMpYhdTUVC/7SDIebjabMTAw\nQD0piFqBvK9kPBwIzraUbHUBjmbfsR45DsfpjfzNrrjiCnz33Xe48847sWTJEoyNjUUtrt/97nd4\n6KGHqC/yTGPeEbIvWJalDR6xlliMcJp0xLw9WDKVSCTQ6/Xo7+9HZmbmtI3DYEEadR0dHRgfH6dl\nhFBUEwRk72BKSsqUBkCBQHby+UqgSCZN3NvILkASZ6gkTfYRZmVlhdVYDOSN4U8rnZO4GheXLodE\nyWFrx2MwOAYhCDyszgmPQuLIzj6ZVIEsTQkMDj2cnANSRoaq9NOQ6ayHMteNg8Yt6Bj7HoBAgTqV\nUAAAIABJREFUF6LaXSYAWhSkVuMnS26bFGcgTwqiRBkdHUVnZydcLpdXM5Y0OQORNHm9hKQHBwep\nPwtZAhCoXDJTiNTpjXghMwwTtbLC+++/D51Oh6VLl0bleMFg3hGyuNNOPHeLi4unlGZFYlIfDOlN\nTEzQxaN1dXVRaYiJb0+Tk5Nx4okn0kbXxMTEJNXEVCRttVrR2toKhmGwZMmSqPq+KhQKLFiwwOtD\n4ltGsFqtXhabhKR9yYDI2GQy2bQytlAxnVbabDaDs8vAOu2QS9WQSzRwclaoZFqcUvIrrF54MRIU\nKWgbbUDPWCOsoxwWJq5EeXk5FAoF2KYR9BsbIZeqqJWnUqbBjae8DY7noJR5my7ZXWZ0je8FIKAw\ndSk0Cg8pB1KikGasb5PTt4QkJlqr1Yqmpiao1WrU1NR4lb7Ed1vAzJc8orG+KRxz+qm2hdx///34\n+OOPw44pHMw7QhYEAd3d3ejr65tSSyzGTBEymWBjGAbp6ekoLCwMmowDZeBT1YnFI8LksWKHL1+S\n1mg0GB8fh8ViQVlZWVgndDiYqoxgMpnQ2dkJq9VKtcoJCQkwmUywWCwoLy+ftTh9tdIpuv+HP27/\nLazsBJxH6sUuN4uxAQu+M34MA98FCadEJlODkxYv9yL33KQKyCVKSCABBIDjXUhT6/DE15eCdVsg\nl6pw8bL7UJxeBws7jte+vxkWdgwAA40iGZfWPoRkdabfOP01Y313+A0ODtK7k8TERLAsC6vViqqq\nqoDv50w0DwMh0o0lDodjWpMvfwi0LeTAgQPo7Oyk2XFfXx9qa2vR0NDgd/9ktMCE2F2N8TxRcOjq\n6kJGRkbQV1xBELBt2zasXr066Oc4cOBAwHFl0mgSE0hzczPS09ODvp1qaGhAbW0tfQ3RatgR7WdX\nVxdGRkboctSp9MexgsvlQldXFwYGBqBSqejttLh+OltOZATtY7vwwrb/PFIvVkKAABfHIkGWCpfL\nU5vVyrKwKuk30GpSvOL8V/tTaOh9FwwjQbomDzanAW7eBYVMDRfHQsJIccPJf8W2rrewu/8juiXb\nwo5hUfZanFN1Q8TxE729RqPxmPuLDO2D8RrxbR768ke4JN3R0YGkpKSwyg2CIODkk0/Gnj17ZqwW\nXlRUhF27dkVSDgkqsHmXIQNAbm7ujJvU+xufJsoEUiZZtGjRUQ/cMDyRiRRoqh12oUA8RpyRkYGT\nTjoJUqk0oP44liRtNBrR0tICrVaL1atX03o2qfWaTCb09vbSWq8vSc+Yw5wgQCVLOGLP6fm3i3PA\nDTccMMDNOWHjx8BkD6Ai5wQvrXQGuwbnLFgJVYIcSo0c77TcBoXMk9XJpUq4OSfGrL0wOcYgZY7W\n76USBSzseERhu1wuapC1fPlyr7KUuH7e398Ps9lM7VfF28NDbR6GMtQS90L2YF4ScrieyKFAPD7N\n8zz6+vrQ29uL/Px8v8qJcAiZdJ5D3WHnD8Q6VKFQTKq/BrrlJTVpX5IW13qjTdLk7sLhcKCqqmqS\njM1frZeoEUwm0yTzIl/JWKTITFwIHrxHbywwcLpZCBBg5o66/3GCCx8efhR1+echKyvLbxlhZELv\n2cbCuiGVyABGgMBwkPEJKE6vQ9vodnC8CwADN+dASVpdWPEKgoDBwUF0dXWhqKgI2dnZk4grUP3c\narXCZDLRRQzEWzqc5qEvUfs2DyOpIUdafw4GXV1dM3p8gnlJyOEilDl60pUmc/kZGRlTKidCqVOT\njKKtrQ1paWm0jhoOWJZFe3s7HTwJ1hHL3/DFTJI02Uk4MDCAkpKSgDI2fwikRiBZn1gy5kvSoX6Q\ntaoF+HHZH/D3w3cDDIcEVRLMrFO04ukIBAEDxiaULKin3/L1lf6p5hZsOfwYGAAcz6M2/UKM9ltg\ntyYhl1mFbst3kEolWJZ7DpbpzgkpTsAj9WxqaoJKpQpZNSMuDR19SUfH2MU9CWLBKR4S8qfSEE8e\nikfDic8FcFSSF0oCQiZI5wPmJSGHc+viO7gxHZxOJ/r6+pCWloba2tppO/5SqRQsy075GHFtrrS0\nlHb3CaGSJabkv6kGLziOQ09PD4aGhqbU6YaCmSLpsbExtLa2IjMzEytXroxKJiuRSPySNNH1Dg4O\n0g3cYoe5pKSkgCTtcDg804BMLv5w2icQpC50ju/GX3bfBofbe5WSAAEqudbvcQhq885BYepSjFp7\nkKrJQWbi0cUCK1wr6N/fYrFg185dQdfPxVajFRUVUWuCBhpjFw8JDQwMTNom4zt8I455fHwcLS0t\nyMnJoWUUf5n0VCQ9X3wsgHlKyOGAZLDTkQGRXtlsNmRmZqKysnLKxxNMVbLw17ALJBfz1fQqFAov\nklYqldQAKCcnx+9gRzQRCkmrVCqvWMnSVolEEnUZmz+IVRM6nQ7A0Vtzs9lMb82JCxl5bEJCAvR6\nPQYHB1FWVualDslMXAgB/BGD+6N+IpWZa5CbVDFtTOkJeUhPyJv0fblcPkmJIq71iuvnYl9pjuPQ\n1taGjIyMGf/bA4GHhPxNSJILClH4DAwMgGVZLF26dJJCYrq6NHDUEW++bAsB5ikhR2LBGSiTczqd\naGtr86wCKiuD2+2G2Tx5weRUx/clZPEJF0zDbiqSNplM6OnpgclkgkKhQGZmJjQaDViWnVWfCWB6\nkh4fH0dTUxOcTifdB0hWUM22ukOcdebm5tJYCUn39vZibGyM1llNJhMAUBvQLG0xzl10Iz489OiR\n3+WwtvQ/cFbltVF/z6fSSk9MTODw4cNUf2y1WtHb2xs1X+lQ4U/aSD4zxP9aJpNBoVCgo6PDK+uX\nyWRBNQ/J1x999BH6+/tn9fXNFOYlIYcDuVzut8bLcRy6urroiG5VVRUYhsHY2FjIi0vFj59qh10o\nUCgUNIOTyWQ44YQTIJfLKUmLfSZ8M+nZJmmVSgWDwYDx8XEUFRVBp9PR212DwUAHGnwz6dkmaYZh\nIJfLMTo6CkEQcOKJJ0KlUtEJubGxMTohp9FokKtdhf+q/SsEhQPZyQuhkidO/yRRjNVut2NgYABF\nRUXIycnxuqCIG3Jk5DpUX+loged59Pf3g+M4rF69GkqlktaPybi92Wymvt3iWr9CoZhE0sPDw7jp\nppsgkUiwefPmWX0tM4V5qUPmOC7kQQ9fnbAgCOjv70d3dzd0Oh0KCgq8SJNImZYsWRLU8c1mMzo7\nO1FdXR11A6CxsTGUlpZ6ZSNiiOt85L/ZJj6TyYSWlhYkJiaipKQkYMYmzqRJeWY2YyWKGXFzMRDE\nC1TJ7bk/r+ZQSjFu3gm704QEZSokzNTlM7vdjqamJigUCpSVlU1JsKQhR2I1m83T+kpHC4IgYGho\nCJ2dnSguLqZ3TYFAxsPJe0o256jVaoyPj2NwcBAWiwUvvvgi7r33Xpx//vnHguQtqADnJSHzPB+y\nWVB7ezsSEhKQlZVFl3Wmp6ejuLjYL3lYLBa0tbVh2bJlQR3fZrPh4MGDWLJkCeRyecR64v7+fiqz\ny83NDTnDFhMfOfFZlg1q1DoUkFKP3W5HeXm5V9c+nFhnkqQNBgNaWlqQnp6OoqKisJqLgd5XsRIh\n0B3KQf1neHPPHRDAQSXT4ooTnoIuefJWaaJI0ev1KC8v9/LkiEas5G6KxBtJyYtlWTQ1NUEmk6G8\nvDzs0gmJ9auvvsKmTZvQ3d2NxMRE6HQ6bNy4EStWrAjruLOIOCGHAiLhMRgMUCqVKCsrm3IU0+Fw\n4NChQ6irm1ofSupcxG7TaDTS20cxmQQrvxobG6NyuIULF0ZVf+lLfCTjEze4gr3VJZlmf38/iouL\nkZmZGdUsZiri8/Vpng5OpxOtra1gWRaVlZVR9fIgsYrvUMxms1cZSavVgpdb8dT2X4AXOEgYKdy8\nCxpFMv5w+scenfIRGI1GejcX7kUjmFjF2amvaiIYQyixa2BZWVnEhj88z+Odd97Bo48+ivvuuw/r\n168HwzDQ6/XQaDTHQlPv+CVkQRDgdDqnf+AR2Gw27Nu3D263G0uXLg1K0+h2u/H999/jhBNOCBiD\nuAEhzohJjU/8AeU4jmpk/W1hJuoOqVQ67cUimhDflpP/SO1UfEERZz7korFgwYIZIY2pYg2FpAVB\nQF9fH/r6+mbkojEdxCTdOPw1vhl5xqPYoOPHwM2nfoBUTTbcbjfa29thNptRWVk5aWBmpiFWTZjN\n5il9pe12OxobG6HRaFBaWhpx0jA4OIjf/e53SEpKwqZNmwKW5uY44oQ8HcjWZIPBgIyMDAiCgLKy\nsqCfI5D/hW/DLpgPOZFfGY1GetIDoEoJl8uFioqKsG9PowlxPZL8x3EclEolHA4H5HI5KioqwipP\nzESs/khaKpXC4XAgOTkZZWVlUc+KQ8WAqQVPffPLo9tIeI/X8o8XPA4JZGBZFpmZmSgoKEBCQsKc\nqJmKfaVNJhOsViv15c7NzUV2dnZEXiM8z+Nvf/sbHn/8cWzcuBHnnXfenHjdYSJOyIHAcRy6u7uh\n1+uxcOFC5OTkYGJiAkNDQ6iqmlyzC4TvvvvOi5CjubGDiPv7+/upzMlqtXrMa0S1yEDbLmYTHMeh\no6MDIyMjyMjIoHpZjuO8SjPhTMZFGy6XCy0tLbBarcjKyoLL5ZqUSYtrp7OJjxo345vONyE94rG8\nftEdUBo9myqysrKo3wjJTsWxxpqkrVYrGhsbodVqkZGRQe8Awx1j1+v1uOGGG5CWlobHH398TiQi\nEeL4JWQAfqfiSF2rq6sLubm5KCgooCcGWZ1TXV0d9HMQQo4mEQuCZ/FqR0cHMjMzUVhY6HXyErIj\nmbTVaqW3jsnJyWEZvocLcfc8Ly8POp1u0qisbyYtHl/2V5qZyVhJTXPhwoXIysryeo9IJk2yvUAk\nPdNywX5jEyZsevAWDWxjwqRBFAKxXanJZAppNVU0QRKH4eFhVFZW+q3l+vpKE2mbeEKSaKV5nseb\nb76JJ598Evfffz/OOeecYzkrFuP4JmSn0+llMESUE6mpqSguLp7UmLLZbGhubsby5cuDfo5vv/0W\nq1atigoRA0f3/alUKpSWlgatGvD9cFqt1pDGrMONtbm5GQkJCSgpKQlajSE2rSEf0JkmabPZjKam\nJiQlJaGkpCQkW9bZJmkSa0pKCoqLi0N6H8jghXjkWjwdR6w1o0XSZrMZjY2NtFcQynHFE5Imkwmt\nra24/fbb6aKC2267DWvXrj1W68X+ECdkQRCo/lUul09ZK3Q6ndi3bx/q6+v9/lwM0rDbtWsXFAoF\n9UzQarVhnexih7NwpWG+EE/wmUwm2O32qAyHkLq71WqNWp1YnEGR21xi/ygm6VDfW5fLhfb2dlgs\nlqjFOpWmezpZ21TgOA7t7e0wGo2orKyMWv3d7XZ7vbek7OVbQgiVTDs6OjAxMeHXkS9U8DyPN954\nA08//TSuvvpqJCUlYc+ePaioqMAVV1wR0bHnEI5vQjaZTGhubgbLsigvL59WFsPzPHbs2IETTzxx\nyseJG3b+1BKh1HhJLZv4J0fDAGgqiInEaDROUiAkJycHzHTFMjZ/t/zRBnFrE5M0AK/3NlC2J7ac\nLCwsRE5OzozGGilJj4yMoK2tDXl5ecjLy5vxW3SxJ4a/Ou9UntIGgwHNzc3Izs5GQUFBxLH29/fj\nuuuug06nwyOPPDJvTIL84Pgm5EOHDiE5OdnvUtNA8G3SiRFsnVhsoG40GmmNV0x6KpUKQ0NDtJad\nn58fk8ZcMLrjpKQk6qU82zI2X4h9jwmR+F4ABUFAa2srLaXMtocDQTAkrVKp0NnZCYZhUFFREdMt\nLeIN1753KaTUQQytqqqqIlal8DyP1157Dc8++yweeughnHnmmfOlVhwIxzchu1wuSp7Bwh8hR6Nh\nRzr5ZFOwyWSCXC5HVlYWUlNTY9LRDwSxpG18fBwjIyMQBAEpKSlITU2lpZlYqyUIyAXQYDBgYGCA\n7lYj7+tsNbeCASFpo9GI/v5+GAwGyOXySUNCs+0zEgiklKTX66lXikwm83KXC+dc6Ovrw7XXXoui\noiI89NBDx8JQRzRw/K5wAiJf5xJN5YRcLodGo0F/fz/kcjlWrVoFmUxGs+i+vr5JY8vJyckxye6I\nCRDxC6ipqUFqaiotzQwNDVEf4VioJXwhkUjAsiz0ej0KCgqg0+m87lK6urq8lCjiUtJskx7DMHC7\n3ejp6UFycjKWLl1K4ycXbHIuxNoMCvBc7Eg8q1atgkql8mrGDQ0Noa2tjcobxSTt79zleR5/+tOf\n8MILL+CRRx7B6aefPicuPHMJ8zZDdrvdIa1MAoBt27Zh5cqVdONzpDvsSBydnZ0YHx+f1gDI4XBQ\nORuZiCPZE5G0zSTpCYKA4eFhdHR0QKfTIS8vL2Bm6duII4MswdR4owWr1Yrm5maqSplK6eF2u73K\nB2ItL/lvJuWCRKtNGmFTNe2mG7WeDZImLnGB1j75xksu2ORCSDyllUolDh8+jKKiItx9990oKSnB\nww8/POtDQxzHYcWKFdDpdNiyZcusPvcRHN8li3Ac33bu3Imqqip6okdysvM8j4GBAfT29qKgoAC5\nubkhH8+3aWgymaKiPvAHIrlTq9XTklsg+NZ4zWazl+wqOTkZGo0mKivjyUWuvLw87EaQPy2vTCaL\nulyQbEQh/YJwt4XPBkk7nU40NTUBACorK8M2lyKlr56eHtx5553Yu3cv1Go1li1bhosuuggXX3xx\n2DGGg8ceewy7du2CyWSKE3IsEAohk/IEUREA8CKRUGuQxMuBGMBEs95KMlOSSYsbWySLDuV2XCxj\nC2XnXrAQa2PFgyzhZKZkaKa9vR06nS5scpsKYs8GIhf01XQH637mdDrR3NwMnudRUVER9T5BNEla\nrEwpKSnx2v4RLrq7u3HNNdegsrISDz74IDQaDTo6OuBwOIK2rY0G+vr6cNlll+GOO+7AY489Fifk\nWCAYx7dAdWKS6YlJz1cp4S9zslgsaG1thUwmQ2lp6awZAHEc55VFW63WaTM9sbFOMLel0QTJTMn7\nS/YFkguKP9IjgztETz6bigR/mm7f1VnieMVTgdEit2Dhu+POZDJNImlisETidTgcaGpqglwuj8gi\nk4Dnebz00kt45ZVX8Pjjj2Pt2rUxrRVfeOGFuO2222A2m/HII4/MaUKet029qTBdw87fFmOxUmJ4\neBg2m42e5AkJCRgfH4fNZkNZWdmsaymlUilSU1O9llmSeI1GIwYHB70GQyQSCYaGhujetdluxsnl\ncqSlpXn5E4hJT6/X03i1Wi3sdjusVisqKyujtrAzFPhbnSXOTIm6Q6lUQq1WY2JiAsnJySFveo4G\n/O248yVp8RYZwFOLLy0tjcpFubOzE9deey0WL16Mb7/9Nuxt6dHCli1bkJmZibq6OnzxxRcxjSUY\nHFcZ8lSWmOHAbrejvb0do6Oj9OQm3qwkG5kr8jDgqAG7y+WCQqGA2+2GWq32ijdWul1/0Ov1aG9v\np9k90fGK453tNUSBQJaLjo6OIiUlBU6nEw6HI+brqALBZrPh0KFDVMZmsVimzaSnAsdxePHFF/Hn\nP/8ZmzZtwsknnzwnFBS33XYbXnvtNchkMqq5/+lPf4rXX399tkM5vksWvo5v4VhiTnVsstk5KyuL\nmhSJNbzkdpznea969EwrD/yB7AUcHR31UnqIvY5JvGKHNqI5nu0M2m63o6WlBQzDoLy83Mu/OJhB\nltm+qIhX2YuHfObC6ixfCIKAnp4e6PV6VFZWTrqb8/Xu8DXS91dO6ujowLXXXoulS5di48aNs5YV\nOxwOnHzyyWBZFm63GxdeeCHuueeegI//4osv5nzJYt4TcjT1xMDR3XAajQYlJSXTfph8m3BEeSCW\nss2U3Ep84cjNzZ1Sxib+HV9fZqLsIPHO1EWFOIcNDQ0FdDnzF68/b+bZsP0km0acTicqKyuD6hkE\nuqhEe3WWP1gsFjQ2NiI1NRULFy4M+kLrj6QHBwfx+eefAwC2b9+Op59+GqecckrUY54K5FxNTEyE\ny+XCmjVrsHnzZqxatcrv4+OEHEOQrC8lJUW0gSF80nM4HGhra4PT6URZWVlEOkqxJtZoNMJms9Em\nESG9SDvyFosFzc3NEcnYCHx9JcQXlWgNWpAsMysrC4WFhRERvtj202g0TnKUI3cq4Wb+giBAr9ej\nu7s7KptGAi1LjVbmz/M8vUOqrKyMipJm165duPfee6lWvr+/H1dffTWuuuqqiI8dDmw2G9asWYNn\nn3024BafGOP4JuSGhgbcdNNN1D2rrq4O9fX1WLp0aUjqB3K7PzIygpKSkpC8MUKBP+OfcOq7Yoez\nmZCxEfgbtCDKDvFFZbr3yuFwoKWlBYIgoLy8fMaUKdEaZLFarWhqaqLriWaqPEIyf/GFUDwoNNVE\nnBgmkwlNTU3IyMiI+EIHeD4Pzz77LP7617/iiSeewJo1a+jPXC7XrJeLOI5DXV0d2tracPXVV+PB\nBx+c1ecPAcc3IRO4XC4cOnQI27dvx86dO7F3715IJBIsX74ctbW1qK+vR3l5+aRsSZwFTTe1NhMI\nVN8NlOWJN1HPtoyNQKyUMBqNtP4ovqiQEo94c3JpaWnESzDDgXjEmsgbA2X+JMscGRlBRUVFTFzJ\npivPkIuLTCajk4EGgwGLFi2KSl23paUF1113HVauXIn77rtv1mSdwcBgMOD888/Hk08+Oasa5xAQ\nJ2R/EAQBFosF33//PSXplpYWLFiwACtWrEBdXR3N2n72s5+huLh4zigP/O3dYxgGCoUCFosFqamp\nUdGRRgviphaJ2el0QiaTwW63Iy0tbdY1xdNBPMhCyknkdaSlpaG4uDjm65LE8GcB63Q64XQ6kZqa\nisLCwohH7t1uN5555hm8/fbbePLJJwM6IsYa9957LzQaDTZs2BDrUPwhTsjBgmTDH3zwAR5//HE4\nnU5kZGRAp9Ohrq4OK1aswPLly5GYmDhnPoiA53a/ubkZTqcTKSkpsNvttHQw1ZBFrMCyLFpaWsCy\nLDIyMmhza7qN27GCy+VCa2srbDYbcnNz6cVFXPOfS++x2+1GW1sbrFYrCgsLvbTH4ZpBNTU14brr\nrsMPfvAD3HPPPXPGlRDw+EjL5XJ67p9xxhm45ZZbcO6558Y6NH+IE3KoeOKJJ1BTU4O1a9eC4zg0\nNzdjx44d2LFjB/bs2QOXy4WamhpK0osWLYpJNio2tvd3u09KByQrJXpYQtKz7SQnngosKSmZZMQv\nXutkNBqpF28k4+uRxkvGiAOVf5xOp5cRlK+GNzk5eVYzf+KXkZ+f79c3RXx3RczpfX1RxCUwt9uN\np556Cn//+9/x9NNPz2qjrLe3F7/61a8wNDQEhmFw1VVX4frrr5/0uP379+Oyyy4Dx3HgeR4XXXQR\n7rrrrlmLM0TECTnasNls2LNnDxoaGtDQ0IDDhw9Dq9VSgq6vr5/RWrN4Aaqv5nW63yPZKCERt9s9\nK3pjo9GI5uZmpKWlhSS1CjS+PtPLXO12OxobG6FSqVBWVhb0hSuQ5nim5Wxki7bL5UJlZWVIGay/\nRufbb7+Nrq4udHR0YNWqVdi8efOs+xUT/+Xa2lqYzWbU1dXhvffew6JFi2Y1jigjTsgzDUEQMDY2\nhoaGBuzYsQMNDQ3U3a2+vh51dXWoq6uj0rtIYLFY0NLSAqVSGdIC1Kli96c3jlZW6nQ66Z7AioqK\nqDSVppILRlo6EGugKyoqojKiPZ2larBKiUAgFpnRWqnldrvx6KOP4rPPPsOJJ56IsbEx7N+/H6++\n+ioWL14c0bEjwfr163HNNddg3bp1MYshCogTcixAFkCSUseuXbtgtVqxaNEirFixAitWrEBNTU1I\nG6U7OjpgMpmC2g0YCfypDoipEslKp7OjFKs9ZmP3nr/SQbB7AgnInriMjIyQtyeHimAGWaZrwrEs\ni+bmZrr6KRpZ9+HDh3Httdfihz/8Ie66664502jt6urCySefjIMHD86YhHOWECfkuQKn04n9+/dT\nkj5w4AAUCgWWL19OSbq0tNSLCMTENhuLOgNBbKpkNBqp6Y+4Hk0IwWTyLJZNTk5GcXFxTHw8/E3C\nuVyuSUMWMpkMLpcLbW1tsNlsqKysjJkRjriGLm7CiTXSWq0WDMPQ2nZpaSkyMjIifm6Xy4VNmzbh\nH//4B5555hmsWLEiCq8oOrBYLDjllFNwxx134Kc//Wmsw4kUcUKeqxAEASaTCTt37qSljvb2duTk\n5KCurg6JiYn47rvvsHHjRpSUlMwpgyIAk+rRLMvS0XSSFc+lmP15jDidTrhcLixYsAD5+flzRtlB\n4DtyT7J/hUKBvLw8pKWlRdzoPHjwIK677jqcccYZuOOOO+ZMVgx4LhTnnnsuzjzzTNx4442xDica\niBPysQRBELBr1y7ceOONGBoaQn5+PoaHh1FeXk6z6GXLls3omqFQQeSCXV1dyMnJgVKppOQxF0yV\n/MFut1Pv39zcXErURNMt9uyYC8tRiUKlv78fpaWldBfjdIMsU8HlcuGxxx7D1q1b8eyzz6K2tnaW\nXk1wEAQBl112GdLS0rBp06ZYhxMtxAn5WMPevXvR39+Pc845B4CnydLY2EgHWPbs2QNBELB06VJK\n0hUVFTHJRs1mM5qbm5GYmIiSkpJJjSmx/wWRss2WqZI/iCcDy8vLvbyYCcQ1dKPRGNF2k2jAZrOh\nsbERiYmJKC0t9ZvBT7Ur0F/d/8CBA7juuuvwox/9CLfffvus2pdefvnl1J/44MGDAR/3zTff4KST\nTkJ1dTW9IN5///04++yzZyvUmUCckOcbiDLi+++/p9K75uZmpKameknvwtnfFyzcbjfa29thMplQ\nUVERUqMlkEpCXI+eidtm4udAVmqFUpoQ19B9h0JI3NFeNioIAlV8VFZWhtzI9Y25v78f//M//4Pk\n5GT09vZi8+bNOOuss2b9Tuurr75CYmIifvWrX01JyPMUcUI+HkAsNknDcOfOndDr9VitvyjoAAAN\nKklEQVS4cCE1VFq+fDmSkpIidiQbGhpCZ2dn2Etb/cF3tJplWdqAI/rocGVh5OJhNptRWVmJxMTE\niOMVxyw2goqWcb7YIrO4uDgqJZN9+/Zhw4YNKCsrQ0FBAXbv3o2ioiI89dRTER87VHR1deHcc8+N\nE3KgB8UJef6B53m0trZi+/btaGhowO7du+liSULSixcvDpo0iMNZNKw8pwMxVRI3s8hoNSG8YDZt\nE41uNC8eU8Xsb/DG98IyVWmJ53l0dnZibGwMVVVVEdm7ErAsi4cffhiff/45nn/+edTU1ER8zEgR\nJ+RpHhQn5OMDLMti7969tB598OBBaDQa1NbW0nq0rwaXOIZNTEygoqJi1ie2CMQTZaQeHWjTNvH3\nkEgkUdPohgNf0x/fRqf4wmI0GtHU1ES3z0QjK967dy+uv/56/OQnP8Hvf//7OWM4FSfkaR4UJ+Tj\nE4IgYGJiAjt37qQk3dXVhby8PNTV1VEyvu+++5CXlzdnlB0EZNM2yUitVit4nofb7aZ2qdGu7UYK\nf9tjWJYFwzDIy8tDZmZmxE5yLMviwQcfxNdff43nnnsO1dXVUXwFkSNOyNM86Hgn5EcffRQbNmzA\nyMhITDx55xJ4nsdXX32F3/3ud+B5HqmpqZiYmPAy+K+pqZlTPriAR/HR1NQErVaL1NRUmk07HA7q\nJUEy6bmSKU5MTKC5uRk5OTlISkqiWbRY2RHsdCTB7t27ccMNN+CCCy7Ahg0b5sxrFSNOyNM86Hgm\n5N7eXlx55ZVoamrC999/f9wTMuDZOyaTyegmCJfLhYMHD9J69P79+yGVSr0M/svKymIyVMFxHNrb\n2+lWGN+6q6+XhNFopGPK4nr0bMbudrvR2toKu92Oqqoqvxe3QNORgZzkHA4HHnjgAWzbtg3PP/98\nTHwntm7diuuvvx4cx+HKK6/ErbfeOukxP//5z/HFF19gdHQUWVlZuOeee3DFFVfMeqwxQpyQp8OF\nF16IO++8E+vXr8euXbvihBwEBEGA2Wz2MvhvbW1FRkaGl/Rupj0sRkZG0NbWhry8vJBKKr5Wn+JV\nTuIlrjMR++joKFpbW8MahWdZ1qvRybIsXnrpJSiVSnzzzTe45JJL8Ic//CEmmnSO41BeXo5//etf\nyMvLQ319Pd58881j3Z0t2gjqjz135ltnGe+//z50Oh2WLl0a61COKTAMg6SkJJx66qk49dRTAXhI\nemBgAA0NDdi+fTuef/55jIyMoKysjDre1dbWRmXTBjHWAYDa2tqQdcsSiQRarRZarRY6nQ6A90BI\nV1cXLRuI9dGRGNA7nU60tLSA47iwYgYApVKJzMxMZGZmAgDduLJ//36sWbMGX3zxBbZs2YLvvvtu\n1huZDQ0NKC0tRXFxMQDg3/7t3/D+++/HCTkMzGtCPv300zE4ODjp+xs3bsT999+Pjz/+OAZRzT8w\nDAOdTofzzz8f559/PgAPyTU1NWHHjh147733cNddd4HjuEkG/8FmdGKT+2gZ6xBIpVKkpKR47ckj\nZQOj0YjBwUHY7faQXeQAYGhoCB0dHSguLkZWVlZU4iULfH/+859j06ZN9D3keT4mo979/f3Iz8+n\n/87Ly8OOHTtmPY75gHlNyJ988onf7x84cACdnZ00O+7r60NtbS0aGhqQnZ09myHOW0ilUixevBiL\nFy/G5ZdfDsAzCrx79240NDRg06ZNaGxsRFJSklepQ6fTTSIVMiyRnJyMlStXzkrNVy6XIz09Henp\n6QCOGtAbjUYYDAZ0d3d7eRuTbJrExrIsmpqaIJVKsWLFiqg02Ox2O/77v/8bu3fvxhtvvIHKykqv\nn8fadyOOyDGvCTkQqqurMTw8TP9dVFQUryHPAjQaDdasWUMbhoIgYHR0lBr8v/766+jr60NhYSFW\nrFiBJUuW4J///CfOOOMMnHrqqVEZlggXDMNApVJBpVLRTFesNR4cHERrayt4nodUKoXdbkdRUVHU\nNshs374dGzZswKWXXopHHnlkTjnT6XQ69Pb20n/39fXRclAcoeG4buoRxIqQb775Znz44YdQKBQo\nKSnBK6+8EpP18nMJPM+jvb0dL730El5++WWUlpbCZrN5GfxXV1fPKatIArvdjsOHD9MlsxaLxcvk\nn2TSwcrYAM9dxX333Ye9e/fij3/8I8rLy2f4VYQOt9uN8vJyfPrpp9DpdKivr8df/vKXmG4ZmYOI\nqyzmOj7++GP88Ic/hEwmwy233AIAePDBB2McVezBcRw2bNiAm266CXl5eXA6ndi3bx/16zh48CCU\nSqWXwX9JSUnMbtnFFpn+1j+5XC6YzWaqkrDZbFTGJjYo8sV3332Hm2++GZdddhmuvfbamGXFb7/9\nNu6++240NjaioaHBr4n9Rx99hBtuuAEcx+Hyyy/HHXfcEYNI5zTihHws4d1338U777yDN954I9ah\nzHkIggCj0ehl8N/R0YHc3FyqjV6xYgUWLFgw45N6VquV1sJLSkqCJk3xVhOj0Qin0wmNRoOdO3ci\nLS0NX3/9NVpaWvDCCy+grKxsRl/DdGhsbIREIsF//ud/4pFHHplTW0WOIcQJ+VjCeeedh4svvhiX\nXnpprEM5JiEIAnp6eihB79y5ExMTE5MM/kMpF0wFshR1eHg4LItMf/FbrVY89dRT+OijjzA+Po7U\n1FRUV1fj0Ucfpc3FWGLt2rVxQg4fcR3yXMBU0rv169fTr2UyGS655JLZDm/egGEYFBYWorCwEBdd\ndBEAT23z0KFD2LFjB9566y3ceuutYBhmksF/qKUAs9mMxsZGpKeno76+PiqlEpvNhnvuuQdNTU14\n6623UFJSAqfTOR+We8YRAuIZcozx6quv4vnnn8enn34KjUYT63DmNUgWumvXLppFNzc3Iy0tzUt6\nF2iKjmwUn5iYQFVVVVT8lQVBwNdff41bb70Vv/71r/Hb3/42JrXwYBKHeIYcEeIli7mOrVu34sYb\nb8SXX34Z1UGHOIIHMd4XG/wPDg6iuLjYy+B/165dsNlsWLJkCQoLC6NS9rBYLLjrrrvQ1taGP/7x\nj1i4cGEUXtHMIU7IESFOyHMdpaWlYFmW1gdXrVqF5557bsafNxgjmOMZPM+jpaUF27dvx7fffost\nW7ZApVJh9erVWLlyJTX4D3fYQxAEfPXVV7j11lvx29/+FlddddUxMdQRJ+SIECfkOCYjbgQTGn7+\n859j7dq1+OUvf4n9+/dTQ6VDhw4hISHBy+C/sLBwWmI1m82488470dXVhRdeeAFFRUWz80L8IFgd\n/Lvvvotrr70WIyMjSElJwbJly/B///d/MYj4mEackOOYjG3btuHuu++mH6gHHngAAHDbbbfFMqw5\ni0D+EIIgYHx83Mvgv7u7G/n5+ZSg6+rqkJqaCoZhIAgCvvjiC9x+++24+uqrceWVV8Y8K47r4GcV\ncZVFHJMRN4IJDYFIk2EYpKen46yzzsJZZ50FwEPeXV1d2L59Oz7//HM8/PDDMJvNKC8vx/DwMNRq\nNT788EMUFBTM5ksIiDPOOIN+vWrVKrzzzjsxjCYOIE7IccQRNUgkEhQXF6O4uBi/+MUvAHim9Pbv\n348PP/wQd911V8yz4kB4+eWXcfHFF8c6jOMecUI+zhA3gpldyOVy6gkdC8R18McW4jXk4wxxI5g4\nxIjr4GcNQdWQ5+b9UxwzBplMhqeeegpnnnkmqqqqcNFFF8WEjHt7e3Hqqadi0aJFWLx4MTZv3jzr\nMRzv2Lp1Kx566CF88MEHcTKeI4hnyHHEBHq9Hnq9HrW1tTCbzairq8N7770Xl99FiDvvvBPvv/8+\nJBIJMjMz8eqrryI3N9fvY2Olgz9OEZe9xXHsYP369bjmmmuwbt26WIdyTMNkMlHviyeeeAKHDx+O\nk+zcQLxkEcexga6uLuzZswcnnHBCrEM55iE2IrJarTNuPxpHdBFXWcQRU1gsFlxwwQXYtGlT3NUs\nSrjjjjvw5z//GcnJyfj8889jHU4cISBesogjZnC5XDj33HNx5pln4sYbb4x1OMcMgpGyAZ4pTIfD\ngXvuuWc2w4vDP+I15DjmLgRBwGWXXYa0tDRs2rQp1uHMS/T09ODss8/GwYMHYx1KHPEachxzGd9+\n+y1ee+01fPbZZ1i2bBmWLVuGjz76KNZhHfNobW2lX7///vuorKyMYTRxhIp4hhxHHPC44K1YsQI6\nnQ5btmyJdThh44ILLkBzczMkEgkKCwvx3HPPxScx5wbi5kJxxBEsNm/ejKqqKphMpliHEhH+93//\nN9YhxBEBQiXkuIYmjnkHhmHyAPwJwEYANwI4N7YRxXG8Il5DjiMOYBOA3wPgYx1IHMc34oQcx3EN\nhmHOBTAsCML3sY4ljjjihBzH8Y4fAPgxwzBdAP4K4IcMw7we25DiOF4RqsoijjjmLRiGWQtggyAI\n8RpyHDFBPEOOI4444pgjiGfIccQRRxxzBPEMOY444ohjjiBOyHHEEUcccwRxQo4jjjjimCOIE3Ic\nccQRxxzB/wdgNDkaz0JajwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f355dc64e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Realizamos a Clusterização, agora com um número selecionado de Clusteres\n",
    "kmeans.n_clusters = 3\n",
    "kmeans.fit(X)\n",
    "clts = kmeans.predict(X)\n",
    "\n",
    "# Visualização das Métricas de Avaliação\n",
    "homoScore = metrics.homogeneity_score(y, clts)\n",
    "complScore = metrics.completeness_score(y, clts)  \n",
    "vMeasureScore = metrics.v_measure_score(y, clts)\n",
    "\n",
    "print(\"### Avaliação ({0} Clusters) ###\".format(kmeans.n_clusters))\n",
    "print(\"Homogeneity: \\t{0:.3}\".format(homoScore))\n",
    "print(\"Completeness: \\t{0:.3}\".format(complScore))\n",
    "print(\"V-Measure: \\t{0:.3}\".format(vMeasureScore))\n",
    "\n",
    "# Plotando uma visualização 3-Dimensional dos Dados, agora com os clusteres designados pelo K-Means\n",
    "# Compare a visualização com os gráficos anteriores\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "ax.scatter(pcaData[:,0], pcaData[:,1], pcaData[:,2], c=clts, cmap=plt.cm.Dark2)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
